Inexact proximal gradient algorithm with random reshuffling for nonsmooth optimization

Proximal gradient algorithms are popularly implemented to achieve convex optimization with nonsmooth regularization. Obtaining the exact solution of the proximal operator for nonsmooth regularization is challenging because errors exist in the computation of the gradient; consequently, the design and...

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Bibliographic Details
Published in:	Science China. Information sciences Vol. 68; no. 1; p. 112201
Main Authors:	Jiang, Xia, Fang, Yanyan, Zeng, Xianlin, Sun, Jian, Chen, Jie
Format:	Journal Article
Language:	English
Published:	Beijing Science China Press 01.01.2025 Springer Nature B.V
Subjects:	Algorithms Basic converters Computational geometry Computer Science Convex analysis Convexity Design optimization Errors Exact solutions Information Systems and Communication Service Neighborhoods Optimization techniques Regularization Research Paper Signal processing compressed sensing inexact computation nonsmooth optimization proximal operator random reshuffling
ISSN:	1674-733X, 1869-1919
Online Access:	Get full text
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